





'''simple docstring'''


import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[str]=None			,	SCREAMING_SNAKE_CASE_ :			int=None )							->     Any:

       """simple docstring"""


       return field(default_factory=lambda: default			,	metadata=SCREAMING_SNAKE_CASE_ )






@dataclass
class     lowerCAmelCase__ :

       '''simple docstring'''
       _lowerCamelCase								=field(
           metadata={"help": "The csv file to plot."}      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "Disable logarithmic scale when plotting"}      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={
               "help": "Whether the csv file has training results or inference results. Defaults to inference results."
           }      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}      , )
       _lowerCamelCase								=list_field(
           default=UpperCAmelCase_      , metadata={"help": "List of model names that are used instead of the ones in the csv file."}    )


def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[int] )							->     Optional[Any]:

       """simple docstring"""


       try:
              int(SCREAMING_SNAKE_CASE_ )
              return True
       except ValueError:
              return False
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Any )							->     str:

       """simple docstring"""


       try:
              float(SCREAMING_SNAKE_CASE_ )
              return True
       except ValueError:
              return False






class     lowerCAmelCase__ :

       '''simple docstring'''

       def __init__( self					:				Dict       ,     a__					:				Optional[int]						):
              UpperCAmelCase  =						args
              UpperCAmelCase  =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

              with open(self.args.csv_file       ,     newline=''''''						) as csv_file:
                     UpperCAmelCase  =						csv.DictReader(a__						)
                     for row in reader:
                            UpperCAmelCase  =						row['''model''']
                            self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']						)						)
                            self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']						)						)
                            if can_convert_to_int(row['''result''']						):
                                   # value is not None
                                   UpperCAmelCase  =						int(row['''result''']						)
                            elif can_convert_to_float(row['''result''']						):
                                   # value is not None
                                   UpperCAmelCase  =						float(row['''result''']						)



       def 			__snake_case      ( self					:				Dict						):
              UpperCAmelCase,					UpperCAmelCase  =						plt.subplots()
              UpperCAmelCase  =						'''Time usage''' if self.args.is_time else '''Memory usage'''
              UpperCAmelCase  =						title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

              if not self.args.no_log_scale:
                     # set logarithm scales
                     ax.set_xscale('''log'''						)
                     ax.set_yscale('''log'''						)

              for axis in [ax.xaxis, ax.yaxis]:
                     axis.set_major_formatter(ScalarFormatter()						)

              for model_name_idx, model_name in enumerate(self.result_dict.keys()						):
                     UpperCAmelCase  =						sorted(set(self.result_dict[model_name]['''bsz''']						)						)
                     UpperCAmelCase  =						sorted(set(self.result_dict[model_name]['''seq_len''']						)						)
                     UpperCAmelCase  =						self.result_dict[model_name]['''result''']

                     ((UpperCAmelCase),					(UpperCAmelCase))  =						(
                         (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                     )

                     UpperCAmelCase  =						(
                         model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
                     )

                     for inner_loop_value in inner_loop_array:
                            if self.args.plot_along_batch:
                                   UpperCAmelCase  =						np.asarray(
                                       [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,     dtype=a__       ,     )
                            else:
                                   UpperCAmelCase  =						np.asarray(
                                       [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,     dtype=np.floataa       ,     )

                            ((UpperCAmelCase),					(UpperCAmelCase))  =						(
                                ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                            )

                            UpperCAmelCase  =						np.asarray(a__       ,     a__						)[: len(a__						)]
                            plt.scatter(
                                a__       ,     a__       ,     label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"						)
                            plt.plot(a__       ,     a__       ,     '''--'''						)

                     title_str += f" {label_model_name} vs."

              UpperCAmelCase  =						title_str[:-4]
              UpperCAmelCase  =						'''Time in s''' if self.args.is_time else '''Memory in MB'''

              # plot
              plt.title(a__						)
              plt.xlabel(a__						)
              plt.ylabel(a__						)
              plt.legend()

              if self.args.figure_png_file is not None:
                     plt.savefig(self.args.figure_png_file						)
              else:
                     plt.show()


def       __snake_case		( )							->     Tuple:

       """simple docstring"""


       UpperCAmelCase  =						HfArgumentParser(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						parser.parse_args_into_dataclasses()[0]
       UpperCAmelCase  =						Plot(args=SCREAMING_SNAKE_CASE_ )
       plot.plot()


if __name__ == "__main__":
    main()



'''simple docstring'''




import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__UpperCAmelCase					={
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

__UpperCAmelCase					={
    "ctrl": 2_5_6,
}

__UpperCAmelCase					={
    "Pregnancy": 1_6_8_6_2_9,
    "Christianity": 7_6_7_5,
    "Explain": 1_0_6_4_2_3,
    "Fitness": 6_3_4_4_0,
    "Saving": 6_3_1_6_3,
    "Ask": 2_7_1_7_1,
    "Ass": 9_5_9_8_5,
    "Joke": 1_6_3_5_0_9,
    "Questions": 4_5_6_2_2,
    "Thoughts": 4_9_6_0_5,
    "Retail": 5_2_3_4_2,
    "Feminism": 1_6_4_3_3_8,
    "Writing": 1_1_9_9_2,
    "Atheism": 1_9_2_2_6_3,
    "Netflix": 4_8_6_1_6,
    "Computing": 3_9_6_3_9,
    "Opinion": 4_3_2_1_3,
    "Alone": 4_4_9_6_7,
    "Funny": 5_8_9_1_7,
    "Gaming": 4_0_3_5_8,
    "Human": 4_0_8_8,
    "India": 1_3_3_1,
    "Joker": 7_7_1_3_8,
    "Diet": 3_6_2_0_6,
    "Legal": 1_1_8_5_9,
    "Norman": 4_9_3_9,
    "Tip": 7_2_6_8_9,
    "Weight": 5_2_3_4_3,
    "Movies": 4_6_2_7_3,
    "Running": 2_3_4_2_5,
    "Science": 2_0_9_0,
    "Horror": 3_7_7_9_3,
    "Confession": 6_0_5_7_2,
    "Finance": 1_2_2_5_0,
    "Politics": 1_6_3_6_0,
    "Scary": 1_9_1_9_8_5,
    "Support": 1_2_6_5_4,
    "Technologies": 3_2_5_1_6,
    "Teenage": 6_6_1_6_0,
    "Event": 3_2_7_6_9,
    "Learned": 6_7_4_6_0,
    "Notion": 1_8_2_7_7_0,
    "Wikipedia": 3_7_5_8_3,
    "Books": 6_6_6_5,
    "Extract": 7_6_0_5_0,
    "Confessions": 1_0_2_7_0_1,
    "Conspiracy": 7_5_9_3_2,
    "Links": 6_3_6_7_4,
    "Narcissus": 1_5_0_4_2_5,
    "Relationship": 5_4_7_6_6,
    "Relationships": 1_3_4_7_9_6,
    "Reviews": 4_1_6_7_1,
    "News": 4_2_5_6,
    "Translation": 2_6_8_2_0,
    "multilingual": 1_2_8_4_0_6,
}






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    set()
    __lowerCamelCase            =    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)							)
        __lowerCamelCase            =    char

    __lowerCamelCase            =    set(UpperCamelCase__							)
    return pairs




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	str				=VOCAB_FILES_NAMES
     lowerCamelCase    :	Optional[int]				=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase    :	Tuple				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase    :	Union[str, Any]				=CONTROL_CODES






     def __init__(					self						:						Optional[Any]    ,							a						:						str    ,							a						:						List[str]    ,							a						:						Optional[int]="<unk>"    ,							**a						:						Any       ):





         """simple docstring"""



         super().__init__(unk_token=a    ,							**a       )

         with open(a    ,							encoding='''utf-8'''       ) as vocab_handle:
             __lowerCamelCase            =    json.load(a       )
         __lowerCamelCase            =    {v: k for k, v in self.encoder.items()}
         with open(a    ,							encoding='''utf-8'''       ) as merges_handle:
             __lowerCamelCase            =    merges_handle.read().split('''\n'''       )[1:-1]
         __lowerCamelCase            =    [tuple(merge.split()       ) for merge in merges]
         __lowerCamelCase            =    dict(zip(a    ,							range(len(a       )       )       )       )
         __lowerCamelCase            =    {}






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         return len(self.encoder       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         return dict(self.encoder    ,							**self.added_tokens_encoder       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Dict       ):





         """simple docstring"""



         if token in self.cache:
             return self.cache[token]
         __lowerCamelCase            =    tuple(a       )
         __lowerCamelCase            =    tuple(list(word[:-1]       ) + [word[-1] + '''</w>''']       )
         __lowerCamelCase            =    get_pairs(a       )

         if not pairs:
             return token

         while True:
             __lowerCamelCase            =    min(a    ,							key=lambda a       : self.bpe_ranks.get(a    ,							float('''inf'''       )       )       )
             if bigram not in self.bpe_ranks:
                 break
             __lowerCamelCase      ,    __lowerCamelCase            =    bigram
             __lowerCamelCase            =    []
             __lowerCamelCase            =    0
             while i < len(a       ):
                 try:
                     __lowerCamelCase            =    word.index(a    ,							a       )
                 except ValueError:
                     new_word.extend(word[i:]       )
                     break
                 else:
                     new_word.extend(word[i:j]       )
                     __lowerCamelCase            =    j

                 if word[i] == first and i < len(a       ) - 1 and word[i + 1] == second:
                     new_word.append(first + second       )
                     i += 2
                 else:
                     new_word.append(word[i]       )
                     i += 1
             __lowerCamelCase            =    tuple(a       )
             __lowerCamelCase            =    new_word
             if len(a       ) == 1:
                 break
             else:
                 __lowerCamelCase            =    get_pairs(a       )
         __lowerCamelCase            =    '''@@ '''.join(a       )
         __lowerCamelCase            =    word[:-4]
         __lowerCamelCase            =    word
         return word






     def   SCREAMING_SNAKE_CASE__  (					self						:						str    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    []

         __lowerCamelCase            =    re.findall(R'''\S+\n?'''    ,							a       )

         for token in words:
             split_tokens.extend(list(self.bpe(a       ).split(''' '''       )       )       )
         return split_tokens






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						Dict       ):





         """simple docstring"""



         return self.encoder.get(a    ,							self.encoder.get(self.unk_token       )       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Optional[Any]       ):





         """simple docstring"""



         return self.decoder.get(a    ,							self.unk_token       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase            =    ''' '''.join(a       ).replace('''@@ '''    ,							''''''       ).strip()
         return out_string






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						str    ,							a						:						Optional[str] = None       ):





         """simple docstring"""



         if not os.path.isdir(a       ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
             return
         __lowerCamelCase            =    os.path.join(
             a    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
         __lowerCamelCase            =    os.path.join(
             a    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

         with open(a    ,							'''w'''    ,							encoding='''utf-8'''       ) as f:
             f.write(json.dumps(self.encoder    ,							indent=2    ,							sort_keys=a    ,							ensure_ascii=a       ) + '''\n'''       )

         __lowerCamelCase            =    0
         with open(a    ,							'''w'''    ,							encoding='''utf-8'''       ) as writer:
             writer.write('''#version: 0.2\n'''       )
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,							key=lambda a       : kv[1]       ):
                 if index != token_index:
                     logger.warning(
                         f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                         ''' Please check that the tokenizer is not corrupted!'''       )
                     __lowerCamelCase            =    token_index
                 writer.write(''' '''.join(a       ) + '''\n'''       )
                 index += 1

         return vocab_file, merge_file

     # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
     #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
     #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
     #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
     #     return ''.join(tokens_generated_so_far)
